Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866909941890023424 |
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| author | Azimi-Abarghouyi, Seyed Mohammad Fischione, Carlo Huang, Kaibin |
| author_facet | Azimi-Abarghouyi, Seyed Mohammad Fischione, Carlo Huang, Kaibin |
| contents | Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the network edge. By leveraging the superposition property of wireless signals, AirFL performs communication and model aggregation of the learning process simultaneously, significantly reducing latency, bandwidth, and energy consumption. This article offers a tutorial treatment of AirFL, presenting a novel classification into three design approaches: CSIT-aware, blind, and weighted AirFL. We provide a comprehensive guide to theoretical foundations, performance analysis, complexity considerations, practical limitations, and prospective research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing Azimi-Abarghouyi, Seyed Mohammad Fischione, Carlo Huang, Kaibin Information Theory Artificial Intelligence Machine Learning Over-the-Air Federated Learning (AirFL) is an emerging paradigm that tightly integrates wireless signal processing and distributed machine learning to enable scalable AI at the network edge. By leveraging the superposition property of wireless signals, AirFL performs communication and model aggregation of the learning process simultaneously, significantly reducing latency, bandwidth, and energy consumption. This article offers a tutorial treatment of AirFL, presenting a novel classification into three design approaches: CSIT-aware, blind, and weighted AirFL. We provide a comprehensive guide to theoretical foundations, performance analysis, complexity considerations, practical limitations, and prospective research directions. |
| title | Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing |
| topic | Information Theory Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.03719 |